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In recent years there has been a burgeoning interest in the use ofcomputational methods to distinguish between elicited speech samples producedby patients with dementia, and those from healthy controls. The differencebetween perplexity estimates from two neural language models (LMs) - onetrained on transcripts of speech produced by healthy participants and the othertrained on transcripts from patients with dementia - as a single feature fordiagnostic classification of unseen transcripts has been shown to producestate-of-the-art performance. However, little is known about why this approachis effective, and on account of the lack of case/control matching in the mostwidely-used evaluation set of transcripts (DementiaBank), it is unclear ifthese approaches are truly diagnostic, or are sensitive to other variables. Inthis paper, we interrogate neural LMs trained on participants with and withoutdementia using synthetic narratives previously developed to simulateprogressive semantic dementia by manipulating lexical frequency. We find thatperplexity of neural LMs is strongly and differentially associated with lexicalfrequency, and that a mixture model resulting from interpolating control anddementia LMs improves upon the current state-of-the-art for models trained ontranscript text exclusively.

A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type